Real-life business process event logs may suffer from significant data quality problems negatively influencing process mining analysis. Over time, a range of approaches has been developed to detect and repair these quality problems. Validation of these approaches tends to be challenging due to the lack of a ground truth. Moreover, the identification and definition of event log quality problems have been tackled mainly through a pattern-based approach, with systematic and extensible methods currently lacking. In this article, we present FLAWD , a formal language for describing event log data quality issues that enables solutions addressing the shortcomings of process mining data quality research identified above. FLAWD can be used to formally describe and possibly reason over event log data quality errors, as well as to guide the development of tools for controlled and sophisticated “polluting” of event logs through which benchmark datasets may be systematically created. We present the abstract syntax grammar of FLAWD and an open-source software tool based on it that allows for the insertion of all so-called event log imperfection patterns in a stochastic manner. We show how FLAWD has been used in our research to generate benchmark datasets and how it can be used to formally describe and replicate a range of errors found in real-life event logs.

While event log data quality is recognized as a crucial concern in process mining, the impact of event log errors on different types of process mining tasks has remained largely unexplored. This paper aims to fill such a gap by analyzing how various errors affect analysis results. In particular, we aim to assess whether and to what extent different types of errors that impact the quality of activity labels affect the performance of predictive process monitoring models, considering the three main tasks of next activity, outcome, and remaining time prediction, using publicly available and simulated event logs. The results of the experiments are used to extract preliminary insights into the design of data preparation pipelines for predictive process monitoring.

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS’s recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles.